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Abstract

Previously, we presented two color mapping methods for the application of daytime colors to fused nighttime (e.g., intensified and longwave infrared or thermal (LWIR)) imagery. These mappings not only impart a natural daylight color appearance to multiband nighttime images but also enhance their contrast and the visibility of otherwise obscured details. As a result, it has been shown that these colorizing methods lead to an increased ease of interpretation, better discrimination and identification of materials, faster reaction times and ultimately improved situational awareness. A crucial step in the proposed coloring process is the choice of a suitable color mapping scheme. When both daytime color images and multiband sensor images of the same scene are available, the color mapping can be derived from matching image samples (i.e., by relating color values to sensor output signal intensities in a sample-based approach). When no exact matching reference images are available, the color transformation can be derived from the first-order statistical properties of the reference image and the multiband sensor image. In the current study, we investigated new color fusion schemes that combine the advantages of both methods (i.e., the efficiency and color constancy of the sample-based method with the ability of the statistical method to use the image of a different but somewhat similar scene as a reference image), using the correspondence between multiband sensor values and daytime colors (sample-based method) in a smooth transformation (statistical method). We designed and evaluated three new fusion schemes that focus on (i) a closer match with the daytime luminances; (ii) an improved saliency of hot targets; and (iii) an improved discriminability of materials. We performed both qualitative and quantitative analyses to assess the weak and strong points of all methods.
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